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Compare the advantages and disadvantages of using a recurrent neural network versus a convolutional neural network.
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Recurrent neural networks are able to capture temporal patterns in data and can be used for text or speech recognition. They are well-suited for tasks that require the algorithm to remember past events. However, they can be slower to train than convolutional neural networks and are prone to degradation over long-term dependencies. Convolutional neural networks are good at identifying local features in an image, are more efficient with data, and can be used for object or facial recognition. However, they tend to struggle with long-term dependencies and have a higher computational cost.